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---
license: apache-2.0
language:
- en
tags:
- sft
pipeline_tag: text-generation
widget:
- text: <prefix>You are a helpful assistant model trained by LAION called Aki</prefix><human>Hi, how are you?<bot>
- text: <human>What's the Earth total population<bot>
- text: <human>你好<bot>
- text: <human>안녕하세요<bot>
- text: <human>こんにちは<bot>
- text: <human>Write a story about future of AI development<bot>
---
# Pythia 1.2B SFT model
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Open Assistant
- **Model type:** Pythia
- **Language(s) (NLP):** English
- **License:** Apache-2.0
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [Open Assistant](https://github.com/LAION-AI/Open-Assistant)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
See the example on the right
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[just read pythia](https://huggingface.co/EleutherAI/pythia-12b#out-of-scope-use)
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "theblackcat102/pythia-1b-deduped-sft"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).half().eval().cuda()
input_text = "<human>What's the earth population?<bot>"
inputs = tokenizer(input_text, return_tensors="pt", padding=True).to(0)
outputs = model.generate(
**inputs,
early_stopping=True,
max_new_tokens=args.max_new_tokens,
do_sample=True,
top_k=args.top_k,
temperature=args.temperature,
pad_token_id=tokenizer.eos_token_id,
# dialogue_collator.py line 36
)
output = tokenizer.decode(outputs[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"])
print(output)
```
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
## Training Procedure
```
deepspeed trainer_sft.py --configs defaults pythia-1b --deepspeed
```
This model was trained for 1000 iterations.
### Training Hyperparameters
```
defaults:
learning_rate: 1e-5
gradient_checkpointing: false
gradient_accumulation_steps: 32
per_device_train_batch_size: 2
per_device_eval_batch_size: 2
weight_decay: 0.00
warmup_steps: 600
eval_steps: 250
save_steps: 250
max_length: 512
num_train_epochs: 2
logging_steps: 10
max_grad_norm: 2.0
save_total_limit: 4
fp16: true
eval_accumulation_steps:
freeze_layer:
datasets:
- gsm8k_hard
- webgpt
- squad_v2
- adversarial_qa
- private_tuning
- oa_translated
- prosocial_dialogue
- math_qa
- wikihow
- joke
- gsm8k
- ted_trans_en-hi
- ted_trans_de-ja
- ted_trans_nl-en
- ted_trans_en-ja
- ted_trans_en-es
- ted_trans_en-ms
- xsum:
fraction: 0.5
- cnn_dailymail:
fraction: 0.5
- multi_news:
fraction: 0.5
- tldr_news:
fraction: 0.5
- scitldr:
fraction: 0.5
- samsum:
fraction: 0.5
- debate_sum:
fraction: 0.5
- billsum:
fraction: 0.5
- wmt2019_zh-en:
fraction: 0.9
- wmt2019_ru-en:
fraction: 0.9
- wmt2019_de-en:
fraction: 0.9
- wmt2019_fr-de:
fraction: 0.9
- essay_instruction
- reddit_eli5
- reddit_askh
- reddit_asks
cache_dir: /fsx/home-theblackcat02/.cache
loss_fn: CrossEntropyLoss
eval_size:
log_dir: "base"
quantization: false
seq2seqmodel: false
poly_eps: 1.0
fuse_gelu: true
log_wandb: true
samples_mixing: true # uses collator that mixes samples in the batch to create a single sample with possible multiple tasks within
verbose: false
pythia-1b:
learning_rate: 5e-6
model_name: EleutherAI/pythia-1b-deduped
weight_decay: 0.01
max_length: 540
fp16: true
warmup_steps: 1000
gradient_accumulation_steps: 20
per_device_train_batch_size: 20
per_device_eval_batch_size: 2
eval_steps: 500
save_steps: 500
```
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# Acknowledgements
- [LAION](https://laion.ai/) & EleutherAI
- [Stability.ai](https://stability.ai/) : this project wouldn't be possible without their compute resource
- [Teams and contributors at Open Assistant](https://github.com/LAION-AI/Open-Assistant/graphs/contributors) : who put their time after their day job or whatever into this project
- [Huggingface](https://huggingface.co/) : For the storage and spaces here
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed] |